The periodogram estimate can be jeopardized by errors caused by aliasing and spectral leakage [1]}, when both the range \(T\)  on which the signal is sampled as well as number of sample points \(n\)  are small.
Determining all peaks in the periodogram at once proves to be a difficult task.
Distinguishing between peaks in the periodogram which actually stem from frequencies and not spectral leakage requires a meticulous setup of tuning parameters such as the minimum distance between peaks or their minimum height when employing algorithms such as findpeaks in Matlab.
Instead we utilize an iterative approach to estimate the peak locations from the periodogram [2]}.
